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A parts list optimized for running 7B–13B LLMs locally while staying quiet and efficient. Upgrade the GPU and RAM when you scale into 70B territory.
Upgrade path: swap GPU to RTX 4090 + bump RAM to 64GB when ready for 70B models.
| Component | Part | Price | Why we picked it |
|---|---|---|---|
| GPU | RTX 4090 | $1,599 | 24GB flagship that unlocks 70B+ models and top tokens/sec. |
| CPU | Ryzen 9 7950X | $499 | 16 cores keep high-throughput inference humming. |
| RAM | 128GB DDR5 | $449 | Headroom for multitasking and heavier frameworks. |
| Motherboard | X670E | $399 | Top-tier AM5 board ready for next-gen GPUs and storage. |
| Storage | 4TB NVMe | $299 | Holds large model libraries and project data. |
| PSU | 1000W | $179 | Delivers stable power for RTX 4090 under load. |
| Case | Premium Case | $149 | High airflow + acoustics for workstation builds. |
Yes. The PSU and case handle up to an RTX 4090 (you'll just need PCIe 5.0 adapters and stronger airflow).
For pure inference workloads, DDR4 is fine. Upgrade to AM5/DDR5 when you move beyond 13B models or need PCIe 5.0 storage.
Start by bumping to a 24GB GPU (RTX 4090) and increasing RAM to 64GB for heavier multitasking.
Expect ~420W under inference load and ~80W at idle. A 750W PSU provides 30% headroom.
RTX 4080 platform for faster 13B–70B experimentation.
RTX 4090 workstation ready for production 70B workloads.